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MedVision Reconstructor

MedVision Reconstructor is a Python application designed for reconstructing medical images via Radon Transform, Filtered Back-Projection (FBP), and Simultaneous Algebraic Reconstruction Technique (SART). The project includes a user-friendly interface built with PySide6 that lets you load medical images, select reconstruction parameters (angle range, filters), and visualize both the sinogram and reconstructed images.

Features

  1. Interactive GUI (PySide6)

    • Intuitive interface for loading images and setting reconstruction parameters.
    • Real-time visualization of sinograms and reconstruction outputs.

  2. Multiple Reconstruction Methods

    • Filtered Back-Projection (FBP) with multiple filters:
      • ramp, shepp-logan, cosine, hamming, hann
    • SART (Simultaneous Algebraic Reconstruction Technique)

  3. Radon Transform & Sinogram Generation

  4. Animation of Incremental Reconstruction

    • Enables an angle-by-angle view of how reconstruction quality evolves.

  5. Test Script

    • A standalone script (test.py) that demonstrates sinogram creation and reconstruction with FBP or SART.

Project Structure

Below is the directory structure for MedVision-Reconstructor:

MedVision-Reconstructor/
 ├── images/                         # Folder containing sample medical images
 ├── medical_env/                    # (Optional) Virtual environment folder
 ├── src/
 │    ├── main.py                    # Main GUI application (PySide6)
 │    ├── MI.py                      # Reconstruction class (Radon, FBP, SART)
 │    └── test.py                    # Test script for demonstration
 ├── .gitignore
 ├── LICENSE
 ├── README.md
 └── requirements.txt

Installation

  1. Clone the Repository

    git clone https://github.com/elifpulukcu/MedVision-Reconstructor.git
cd MedVision-Reconstructor

  2. (Optional) Create & Activate a Virtual Environment

    python -m venv medical_env
source medical_env/bin/activate      # On Unix / Mac
# or
medical_env\Scripts\activate         # On Windows

  3. Install Dependencies

    pip install --upgrade pip
pip install -r requirements.txt

    If no requirements.txt exists, install the main libraries manually:

    pip install PySide6 matplotlib scikit-image numpy

Usage

1. Launching the GUI

Run the main application to open the GUI:

cd src
python main.py

Key Interface Elements:

  1. Load Image
    • Select .png, .jpg, .jpeg, or .bmp images from your filesystem.
  2. Maximum Angle
    • Enter the maximum angle (e.g., 180) for radon transform sampling.
  3. Reconstruction Method
    • Choose among the following filters for FBP:
      • ramp, shepp-logan, cosine, hamming, hann
    • Or select SART for algebraic reconstruction.
  4. Calculate
    • Computes the sinogram and reconstructs the image based on the selected method.
    • Displays the sinogram and reconstructed image.
  5. Show Animation
    • Iterates through angles in steps (from 10° up to the specified angle) to visualize progressive reconstruction.

2. Test Script

A separate test script (test.py) demonstrates the reconstruction steps in a non-GUI fashion:

cd src
python test.py

This script will:

  • Load a specified image from the images/ directory (you can change the path in the code).
  • Generate a sinogram using the Radon Transform.
  • Perform Filtered Back-Projection (FBP) and SART reconstructions.
  • Display the original image, the sinogram, and reconstruction results side by side.

Implementation Details

MI.py (Core Reconstruction Logic)

  1. Class MI
    • Constructor: Accepts image, maxAngle, and filterName. Converts RGB to grayscale if necessary.
    • processImage: Rescales the image (default scale factor of 0.4) and prepares angles for radon transform.
    • radonTransform: Performs the Radon Transform (scikit-image.transform.radon).
    • filteredBackProjection: Uses iradon with the specified filter to reconstruct the image from its sinogram.
    • sart: Applies iradon_sart for algebraic reconstruction.

main.py (GUI)

  1. PySide6 Window
    • Load Image button uses a QFileDialog to select and display an image.
    • Calculate calls either filteredBackProjection or sart, then renders the sinogram and reconstruction with matplotlib.
    • Show Animation demonstrates incremental reconstruction from small angles up to the user-specified maximum.

test.py (Testing & Demonstration)

  1. Command-Line Runner
    • Reads an image from images/.
    • Generates sinogram and performs both FBP and SART reconstructions.
    • Displays comparative results via matplotlib.

Planned Improvements

  1. Algorithmic Expansion

    • Investigate other iterative methods like ART, TV-based methods, or deep-learning approaches.

  2. Performance Optimization

    • Potential GPU acceleration using CuPy or PyTorch for large-scale reconstructions.

  3. Dataset & Demo

    • Include more diverse images in images/ to showcase various organ/tissue reconstructions.

  4. Parameter Tuning

    • User-adjustable SART parameters (relaxation, iterations) through the GUI.

  5. CLI Extension

    • Offer a robust command-line interface for batch processing or pipeline integration.

License

Distributed under the MIT License. Feel free to use, modify, and distribute this code for personal or commercial projects, subject to the terms of the license.

Disclaimer

This repository is intended for educational and research purposes. The authors do not guarantee the suitability of this software for clinical or diagnostic use.

Author

Developed by Elif Pulukçu.
Contributions and pull requests are welcome—please open an issue or submit a PR for improvements.